The Experts below are selected from a list of 7272 Experts worldwide ranked by ideXlab platform
Mainak Biswas - One of the best experts on this subject based on the ideXlab platform.
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support vector machine svm based compression Artifact Reduction technique
Journal of The Society for Information Display, 2007Co-Authors: Mainak Biswas, Sanjeev Kumar, T Q Nguyen, Nikhil BalramAbstract:— A compression Artifact-Reduction algorithm based on support vector regression is proposed. The algorithm belongs to a broad family of standard reconstruction methods, but a standardization model is determined from a set of training samples of original images and the corresponding noise-corrupted version. As opposed to Artifact-Reduction methods specific to each type of compression Artifact (e.g., blocking, ringing, etc.), we treat such Artifacts as a manifestation of the same problem, which is the quantization of DCT coefficients. In the testing step, the algorithm tries to undo the effect of quantization by using the relationship between the original and Artifact-corrupted image, determined during the training step. Experimental results exhibit significant Reduction in all types of compression Artifacts.
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Support Vector Machine (SVM) based compression Artifact‐Reduction technique
Journal of the Society for Information Display, 2007Co-Authors: Mainak Biswas, Sanjeev Kumar, T Q Nguyen, Nikhil BalramAbstract:— A compression Artifact-Reduction algorithm based on support vector regression is proposed. The algorithm belongs to a broad family of standard reconstruction methods, but a standardization model is determined from a set of training samples of original images and the corresponding noise-corrupted version. As opposed to Artifact-Reduction methods specific to each type of compression Artifact (e.g., blocking, ringing, etc.), we treat such Artifacts as a manifestation of the same problem, which is the quantization of DCT coefficients. In the testing step, the algorithm tries to undo the effect of quantization by using the relationship between the original and Artifact-corrupted image, determined during the training step. Experimental results exhibit significant Reduction in all types of compression Artifacts.
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compression Artifact Reduction using support vector regression
International Conference on Image Processing, 2006Co-Authors: Sanjeev Kumar, T Q Nguyen, Mainak BiswasAbstract:In this paper, we propose a compression Artifact Reduction algorithm based on v support vector regression. It belongs to the broad family of regularized reconstruction methods but regularization model is learned from a set of training samples of original images and corresponding noise corrupted version. As opposed to Artifact Reduction methods specific to each type of compression Artifact (e.g. blocking, ringing etc), we treat such different Artifacts as symptoms of the same problem, quantization of DCT coefficients. In the testing step, algorithm tries to undo the effect of quantization using information (relationship between original and Artifact-corrupted image) learned during the training step. Experimental results exhibit significant Reduction in all types of compression Artifacts.
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ICIP - Compression Artifact Reduction using Support Vector Regression
2006 International Conference on Image Processing, 2006Co-Authors: Sanjeev Kumar, T Q Nguyen, Mainak BiswasAbstract:In this paper, we propose a compression Artifact Reduction algorithm based on v support vector regression. It belongs to the broad family of regularized reconstruction methods but regularization model is learned from a set of training samples of original images and corresponding noise corrupted version. As opposed to Artifact Reduction methods specific to each type of compression Artifact (e.g. blocking, ringing etc), we treat such different Artifacts as symptoms of the same problem, quantization of DCT coefficients. In the testing step, algorithm tries to undo the effect of quantization using information (relationship between original and Artifact-corrupted image) learned during the training step. Experimental results exhibit significant Reduction in all types of compression Artifacts.
Sanjeev Kumar - One of the best experts on this subject based on the ideXlab platform.
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support vector machine svm based compression Artifact Reduction technique
Journal of The Society for Information Display, 2007Co-Authors: Mainak Biswas, Sanjeev Kumar, T Q Nguyen, Nikhil BalramAbstract:— A compression Artifact-Reduction algorithm based on support vector regression is proposed. The algorithm belongs to a broad family of standard reconstruction methods, but a standardization model is determined from a set of training samples of original images and the corresponding noise-corrupted version. As opposed to Artifact-Reduction methods specific to each type of compression Artifact (e.g., blocking, ringing, etc.), we treat such Artifacts as a manifestation of the same problem, which is the quantization of DCT coefficients. In the testing step, the algorithm tries to undo the effect of quantization by using the relationship between the original and Artifact-corrupted image, determined during the training step. Experimental results exhibit significant Reduction in all types of compression Artifacts.
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Support Vector Machine (SVM) based compression Artifact‐Reduction technique
Journal of the Society for Information Display, 2007Co-Authors: Mainak Biswas, Sanjeev Kumar, T Q Nguyen, Nikhil BalramAbstract:— A compression Artifact-Reduction algorithm based on support vector regression is proposed. The algorithm belongs to a broad family of standard reconstruction methods, but a standardization model is determined from a set of training samples of original images and the corresponding noise-corrupted version. As opposed to Artifact-Reduction methods specific to each type of compression Artifact (e.g., blocking, ringing, etc.), we treat such Artifacts as a manifestation of the same problem, which is the quantization of DCT coefficients. In the testing step, the algorithm tries to undo the effect of quantization by using the relationship between the original and Artifact-corrupted image, determined during the training step. Experimental results exhibit significant Reduction in all types of compression Artifacts.
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compression Artifact Reduction using support vector regression
International Conference on Image Processing, 2006Co-Authors: Sanjeev Kumar, T Q Nguyen, Mainak BiswasAbstract:In this paper, we propose a compression Artifact Reduction algorithm based on v support vector regression. It belongs to the broad family of regularized reconstruction methods but regularization model is learned from a set of training samples of original images and corresponding noise corrupted version. As opposed to Artifact Reduction methods specific to each type of compression Artifact (e.g. blocking, ringing etc), we treat such different Artifacts as symptoms of the same problem, quantization of DCT coefficients. In the testing step, algorithm tries to undo the effect of quantization using information (relationship between original and Artifact-corrupted image) learned during the training step. Experimental results exhibit significant Reduction in all types of compression Artifacts.
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ICIP - Compression Artifact Reduction using Support Vector Regression
2006 International Conference on Image Processing, 2006Co-Authors: Sanjeev Kumar, T Q Nguyen, Mainak BiswasAbstract:In this paper, we propose a compression Artifact Reduction algorithm based on v support vector regression. It belongs to the broad family of regularized reconstruction methods but regularization model is learned from a set of training samples of original images and corresponding noise corrupted version. As opposed to Artifact Reduction methods specific to each type of compression Artifact (e.g. blocking, ringing etc), we treat such different Artifacts as symptoms of the same problem, quantization of DCT coefficients. In the testing step, algorithm tries to undo the effect of quantization using information (relationship between original and Artifact-corrupted image) learned during the training step. Experimental results exhibit significant Reduction in all types of compression Artifacts.
Naveen Subhas - One of the best experts on this subject based on the ideXlab platform.
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combined dual energy and single energy metal Artifact Reduction techniques versus single energy techniques alone for lesion detection near an arthroplasty
American Journal of Roentgenology, 2020Co-Authors: Suraj Chandrasekar, Nancy A Obuchowski, Andrew N Primak, Ceylan Colak, Wadih Karim, Naveen SubhasAbstract:OBJECTIVE. The purpose of this study was to compare a combined dual-energy CT (DECT) and single-energy CT (SECT) metal Artifact Reduction technique with a SECT metal Artifact Reduction technique fo...
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imaging of arthroplasties improved image quality and lesion detection with iterative metal Artifact Reduction a new ct metal Artifact Reduction technique
American Journal of Roentgenology, 2016Co-Authors: Naveen Subhas, Joshua M Polster, Nancy A Obuchowski, Andrew N Primak, F Dong, Brian R Herts, Joseph P IannottiAbstract:OBJECTIVE. The purpose of this study was to compare iterative metal Artifact Reduction (iMAR), a new single-energy metal Artifact Reduction technique, with filtered back projection (FBP) in terms of attenuation values, qualitative image quality, and streak Artifacts near shoulder and hip arthroplasties and observer ability with these techniques to detect pathologic lesions near an arthroplasty in a phantom model. MATERIALS AND METHODS. Preoperative and postoperative CT scans of 40 shoulder and 21 hip arthroplasties were reviewed. All postoperative scans were obtained using the same technique (140 kVp, 300 quality reference mAs, 128 × 0.6 mm detector collimation) on one of three CT scanners and reconstructed with FBP and iMAR. The attenuation differences in bones and soft tissues between preoperative and postoperative scans at the same location were compared; image quality and streak Artifact for both reconstructions were qualitatively graded by two blinded readers. Observer ability and confidence to detec...
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Iterative metal Artifact Reduction: evaluation and optimization of technique.
Skeletal radiology, 2014Co-Authors: Naveen Subhas, Andreas Krauss, Joshua M Polster, Nancy A Obuchowski, Andrew N Primak, Amit Gupta, Joseph P IannottiAbstract:Objective Iterative metal Artifact Reduction (IMAR) is a sinogram inpainting technique that incorporates high-frequency data from standard weighted filtered back projection (WFBP) reconstructions to reduce metal Artifact on computed tomography (CT). This study was designed to compare the image quality of IMAR and WFBP in total shoulder arthroplasties (TSA); determine the optimal amount of WFBP high-frequency data needed for IMAR; and compare image quality of the standard 3D technique with that of a faster 2D technique.
Jiebo Luo - One of the best experts on this subject based on the ideXlab platform.
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ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction
IEEE transactions on medical imaging, 2019Co-Authors: Haofu Liao, Wei-an Lin, S. Kevin Zhou, Jiebo LuoAbstract:Current deep neural network based approaches to computed tomography (CT) metal Artifact Reduction (MAR) are supervised methods that rely on synthesized metal Artifacts for training. However, as synthesized data may not accurately simulate the underlying physical mechanisms of CT imaging, the supervised methods often generalize poorly to clinical applications. To address this problem, we propose, to the best of our knowledge, the first unsupervised learning approach to MAR. Specifically, we introduce a novel Artifact disentanglement network that disentangles the metal Artifacts from CT images in the latent space. It supports different forms of generations (Artifact Reduction, Artifact transfer, and self-reconstruction, etc.) with specialized loss functions to obviate the need for supervision with synthesized data. Extensive experiments show that when applied to a synthesized dataset, our method addresses metal Artifacts significantly better than the existing unsupervised models designed for natural image-to-image translation problems, and achieves comparable performance to existing supervised models for MAR. When applied to clinical datasets, our method demonstrates better generalization ability over the supervised models. The source code of this paper is publicly available at https:// github.com/liaohaofu/adn .
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adversarial sparse view cbct Artifact Reduction
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Haofu Liao, Zhimin Huo, William J Sehnert, Shaohua Kevin Zhou, Jiebo LuoAbstract:We present an effective post-processing method to reduce the Artifacts from sparsely reconstructed cone-beam CT (CBCT) images. The proposed method is based on the state-of-the-art, image-to-image generative models with a perceptual loss as regulation. Unlike the traditional CT Artifact-Reduction approaches, our method is trained in an adversarial fashion that yields more perceptually realistic outputs while preserving the anatomical structures. To address the streak Artifacts that are inherently local and appear across various scales, we further propose a novel discriminator architecture based on feature pyramid networks and a differentially modulated focus map to induce the adversarial training. Our experimental results show that the proposed method can greatly correct the cone-beam Artifacts from clinical CBCT images reconstructed using 1/3 projections, and outperforms strong baseline methods both quantitatively and qualitatively.
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Artifact Reduction in low bit rate dct based image compression
IEEE Transactions on Image Processing, 1996Co-Authors: Jiebo Luo, Chang Wen Chen, Kevin J Parker, Thomas S HuangAbstract:This correspondence presents a scheme for Artifact Reduction of low bit rate discrete-cosine-transform-compressed (DCT-compressed) images. First, the DC coefficients are calibrated using gradient continuity constraints. Then, an improved Huber-Markov-random-field-based (HMRF-based) smoothing is applied. The constrained optimization is implemented by the iterative conditional mode (ICM). Final reconstructions of typical images with improvements in both visual quality and peak signal-to-noise ratio (PSNR) are also shown.
Nikhil Balram - One of the best experts on this subject based on the ideXlab platform.
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support vector machine svm based compression Artifact Reduction technique
Journal of The Society for Information Display, 2007Co-Authors: Mainak Biswas, Sanjeev Kumar, T Q Nguyen, Nikhil BalramAbstract:— A compression Artifact-Reduction algorithm based on support vector regression is proposed. The algorithm belongs to a broad family of standard reconstruction methods, but a standardization model is determined from a set of training samples of original images and the corresponding noise-corrupted version. As opposed to Artifact-Reduction methods specific to each type of compression Artifact (e.g., blocking, ringing, etc.), we treat such Artifacts as a manifestation of the same problem, which is the quantization of DCT coefficients. In the testing step, the algorithm tries to undo the effect of quantization by using the relationship between the original and Artifact-corrupted image, determined during the training step. Experimental results exhibit significant Reduction in all types of compression Artifacts.
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Support Vector Machine (SVM) based compression Artifact‐Reduction technique
Journal of the Society for Information Display, 2007Co-Authors: Mainak Biswas, Sanjeev Kumar, T Q Nguyen, Nikhil BalramAbstract:— A compression Artifact-Reduction algorithm based on support vector regression is proposed. The algorithm belongs to a broad family of standard reconstruction methods, but a standardization model is determined from a set of training samples of original images and the corresponding noise-corrupted version. As opposed to Artifact-Reduction methods specific to each type of compression Artifact (e.g., blocking, ringing, etc.), we treat such Artifacts as a manifestation of the same problem, which is the quantization of DCT coefficients. In the testing step, the algorithm tries to undo the effect of quantization by using the relationship between the original and Artifact-corrupted image, determined during the training step. Experimental results exhibit significant Reduction in all types of compression Artifacts.
